Instructions to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("OBLITERATUS/Qwen3.8-27B-OBLITERATED")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M

Use Docker

docker model run hf.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M

LM Studio
Jan

vLLM

How to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OBLITERATUS/Qwen3.8-27B-OBLITERATED"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OBLITERATUS/Qwen3.8-27B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M

Ollama
How to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with Ollama:
```
ollama run hf.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M
```
Unsloth Desktop

MLX LM

How to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "OBLITERATUS/Qwen3.8-27B-OBLITERATED"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OBLITERATUS/Qwen3.8-27B-OBLITERATED"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "OBLITERATUS/Qwen3.8-27B-OBLITERATED",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with Docker Model Runner:
```
docker model run hf.co/OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M
```

Lemonade

How to use OBLITERATUS/Qwen3.8-27B-OBLITERATED with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OBLITERATUS/Qwen3.8-27B-OBLITERATED:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-OBLITERATED-Q4_K_M

List all available models

lemonade list

Atomic Chat

Qwen3.8-27B-OBLITERATED / generation_config.json

pliny-the-prompter

Fix: remove pad token from eos_token_id list (caused early stopping)

5350c98 verified 12 days ago

Raw

History Blame Contribute Delete

165 Bytes

	{
	"bos_token_id": 248044,
	"do_sample": true,
	"eos_token_id": [
	248046
	],
	"pad_token_id": 248044,
	"temperature": 1.0,
	"top_k": 20,
	"top_p": 0.95
	}